Scalable earthquake magnitude prediction using spatio-temporal data and model versioning

Earthquake magnitude prediction is critical for natural calamity prevention and mitigation, significantly reducing casualties and economic losses through timely warnings. This study introduces a novel approach by using spatio-temporal data from seismic records obtained from the Indian government seismology department and weather data sourced via VisualCrossing to predict earthquake magnitudes. By integrating environmental and seismic variables, the study explores their interrelationships to enhance predictive capabilities. The proposed framework incorporates a machine learning operations (MLOps)-driven pipeline using MLflow for automated data ingestion, preprocessing, model versioning, tracking, and deployment. This novel integration ensures adaptability to evolving datasets and facilitates dynamic model selection for optimal performance. Multiple machine learning algorithms, including Gradient Boosting, Light Gradient Boosting Machine (LightGBM), XGBoost, and Random Forest, are evaluated on dataset sizes of 20%, 35%, 65%, and 100%, with performance metrics such as Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, and R². The results reveal that Gradient Boosting performs optimally on smaller datasets, while LightGBM demonstrates superior accuracy with larger datasets, showcasing the pipeline's flexibility and scalability. This research presents a scalable, robust, and resilient solution for earthquake magnitude prediction by combining diverse data sources with a dynamic and operational MLOps framework. The outcomes illustrate the potential of integrating advanced machine learning techniques with lifecycle management practices to enhance prediction accuracy and applicability in real-world seismic scenarios.